Personalized Rituximab Treatment Based on Artificial Intelligence in Membranous Nephropathy (iRITUX)

Centre Hospitalier Universitaire de Nice

Status and phase: Not yet enrolling; Phase 3

Conditions: Membranous Nephropathy

Treatments: Drug: RiTUXimab Injection

Study type: Interventional

Funder types: Other

Identifiers

NCT06341205

22-APN-01

Details and patient eligibility

About

Membranous nephropathy is an autoimmune disease affecting the kidney, and the most common cause of nephrotic syndrome in non-diabetic Caucasian adults. The course of this disease is highly variable from one individual to another, ranging from spontaneous remission to progressive chronic kidney disease.

The identification of autoantibodies - e.g., the phospholipase A2 receptor type 1 (PLA2R1) - has promoted the use of immunosuppressive drugs such as rituximab which is now a safe and effective first-line treatment for the management of membranous nephropathy. However, up to 40% of patients do not respond to a first course of rituximab treatment. In nephrotic patients, due to urinary drug loss, rituximab blood level is lower than in other autoimmune diseases treated with rituximab without proteinuria. This high urinary drug loss decreases the drug exposure, potentially explaining why rituximab regimen with low dose infusions (375 mg/m2) did not demonstrate efficacy after month-6 compared to a non-immunosuppressive antiproteinuric treatment in a previous study. In contrast, a regimen of two 1-g infusions two weeks apart was associated with a significantly greater remission rate after 6 months.

Recently, the investigators have shown that after two 1-g rituximab infusions, the rituximab blood level 3 months after the first rituximab infusion, was correlated with the likelihood of remission after 6 and 12 months of the rituximab treatment. Patients with positive rituximab blood level 3 months after treatment had a higher chance of remission at month-6 and at month-12 than patients with an undetectable rituximab level at month-3.

Nowadays, machine learning algorithms are increasingly used in medicine, especially in pharmacology, to predict the exposure to a drug, the initial dose to administer or the interval between two infusions.

The objective of this study is to use a machine learning algorithm predicting the risk of having an undetectable residual level of rituximab 3 months after treatment, in order to propose a personalized treatment management with early additional doses of rituximab for the patients at risk.

Enrollment: 120 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Age ≥ 18 years
• Ongoing episode of membranous nephropathy diagnosed by the presence of anti-PLA2R1 antibodies detected by ELISA (≥ 14 RU/ml, EUROIMMUN): the result must be validated by the Coordination team before randomization.
• Nephrotic syndrome defined by proteinuria > 3.5 g/24h (or UPCR > 3.5 g/g) and serum albumin < 30 g/L at diagnosis
• Estimated Glomerular Filtration Rate (CKD-EPI formula) > 30 mL/min/1,73 m2
• Indication for rituximab treatment according to the KDIGO and French guidelines
• Non-immunosuppressive antiproteinuric treatment at stable dose for 2 weeks according to French guidelines, including a renin angiotensin aldosterone system inhibitor, a diuretic and a low-salt diet at maximal tolerated dose (i.e., absence of orthostatic hypotension and no increase in creatinine > 30%)

Exclusion criteria

• Secondary Membranous nephropathy related to cancer, infection, systemic lupus, drug
• Diagnosis of PLA2R1-associated Membranous nephropathy not confirmed by the Coordination team (validation mandatory for randomization)
• Pregnancy or breastfeeding
• Immunosuppressive treatment (including rituximab) in the 6 months preceding inclusion
• Presence of anti-rituximab antibodies detected by Central Lab
• Cancer under treatment
• Patients with active, severe infections
• Hypersensitivity to the active substance or excipients
• Patients severely immunocompromised
• Severe heart failure or severe, uncontrolled cardiac disease

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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120 participants in 2 patient groups

Standard-of-care

Active Comparator group

Description:

rituximab treatment 1gram x 2 (day-0, day-15)

Treatment:

Drug: RiTUXimab Injection

Personalised treatment

Experimental group

Description:

personalized treatment based on the algorithm for assessing the risk of having undetectable rituximab level after 3 months: * Patients with a risk between 0 and 50% will receive 1gram x2 (day-0, day-15) * Patients with a risk between 51 and 75% will receive 1gram x 3 (day-0, day-15, day-30) * Patients with a risk between 76 and 100% will receive 1gram x 4 (day-0, day-15, day-30, day-45)

Treatment:

Drug: RiTUXimab Injection